{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Neural Networks: MNIST classification with Keras\n",
    "\n",
    "This is an annotated version of Keras's [example MNIST CNN code](https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py).\n",
    "\n",
    "The MNIST classification task is a classic machine learning benchmark. The data includes 70,000 handwritten grayscale digits, and the task is to identify them. The digits run from 0 to 9, so this is a multiclass classification problem. There are 10 possible classes, one for each digit.\n",
    "\n",
    "The MNIST classification task is sort of like a \"hello world\" for computer vision, so a solution can be implemented quickly with an off-the-shelf machine learning library.\n",
    "\n",
    "Since convolutional neural networks have thus far proven to be the best at computer vision tasks, we'll use the Keras library to implement a convolutional neural net as our solution. Keras provides a well-designed and readable API on top of both Theano and TensorFlow fast backends, so we'll be done in a surprisingly short amount of steps!\n",
    "\n",
    "Because MNIST is such a common task, the dataset is included with many machine learning libraries. With Keras, you can load the dataset with just a couple of lines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(60000, 10000, 28, 28)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.datasets import mnist\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "\n",
    "n_train, height, width = X_train.shape\n",
    "n_test, _, _ = X_test.shape\n",
    "\n",
    "n_train, n_test, height, width"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 60,000 28x28 training grayscale images and 10,000 28x28 test grayscale images. Let's visualize some of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa2b413fed0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jKBQK2Gw2GI3GCzdAJpPBwcEByz4FAgEEAgEWmNbrdSb7PC/NJbOim3Ixczqd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URQKpVYdyIQCFAul7G9vY1IJIJ8Pg+fz4e+vj6IRCJ2jlQqlfjyyy+RTqdRKBSu9FAiFot5qkT6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\nYJEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2b8769350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "samples = np.concatenate([np.concatenate([X_train[i].reshape(28,28) for i in [int(random.random() * len(X_train)) for i in range(16)]], axis=1) for i in range(4)], axis=0)\n",
    "plt.figure(figsize=(16,4))\n",
    "plt.imshow(samples, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some preprocessing steps are required to get the data into the proper form for the network. The training and test data need to be shaped as (`n`, `w`, `h`, `c`) where `n` is the number of samples, `w` and `h` are the spatial dimensions (width and height) of the samples, and `c` is the number of channels (1 for our grayscale MNIST images, 3 for RGB images). The labels need to be converted into one-hot vectors, which are `nc`-element arrays (`nc` is the number of classes) which are 0 for all classes except 1 for the class the label is assigned to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.utils.np_utils import to_categorical\n",
    "\n",
    "# we have to preprocess the data into the right shape\n",
    "X_train = X_train.reshape(n_train, height, width, 1).astype('float32')\n",
    "X_test = X_test.reshape(n_test, height, width, 1).astype('float32')\n",
    "\n",
    "# normalize from [0, 255] to [0, 1]\n",
    "X_train /= 255\n",
    "X_test /= 255\n",
    "\n",
    "# numbers 0-9, so ten classes\n",
    "n_classes = 10\n",
    "\n",
    "# convert integer labels into one-hot vectors\n",
    "y_train = to_categorical(y_train, n_classes)\n",
    "y_test = to_categorical(y_test, n_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keras makes it very easy to define a neural network. We first instantiate a sequential Keras model, meaning the components of the model come one after the other - e.g. layer by layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The general architecture of a convolutional neural network is:\n",
    "\n",
    "- convolution layers, followed by pooling layers\n",
    "- fully-connected layers\n",
    "- a final fully-connected softmax layer\n",
    "\n",
    "We'll follow this same basic structure and interweave some other components, such as [dropout](https://en.wikipedia.org/wiki/Dropout_(neural_networks), to improve performance.\n",
    "\n",
    "To begin, we start with our convolution layers. We first need to specify some architectural hyperparemeters:\n",
    "\n",
    "- _How many filters do we want for our convolution layers?_ Like most hyperparameters, this is chosen through a mix of intuition and tuning. A rough rule of thumb is: the more complex the task, the more filters. (Note that we don't need to have the same number of filters for each convolution layer, but we are doing so here for convenience.)\n",
    "- _What size should our convolution filters be?_ We don't want filters to be too large or the resulting matrix might not be very meaningful. For instance, a useless filter size in this task would be a 28x28 filter since it covers the whole image. We also don't want filters to be too small for a similar reason, e.g. a 1x1 filter just returns each pixel.\n",
    "- _What size should our pooling window be?_ Again, we don't want pooling windows to be too large or we'll be throwing away information. However, for larger images, a larger pooling window might be appropriate (same goes for convolution filters)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# number of convolutional filters\n",
    "n_filters = 32\n",
    "\n",
    "# convolution filter size\n",
    "# i.e. we will use a n_conv x n_conv filter\n",
    "n_conv = 3\n",
    "\n",
    "# pooling window size\n",
    "# i.e. we will use a n_pool x n_pool pooling window\n",
    "n_pool = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can begin adding our convolution and pooling layers.\n",
    "\n",
    "We're using only two convolutiona layers because this is a relatively simple task. Generally for more complex tasks you may want more convolution layers to extract higher and higher level features.\n",
    "\n",
    "For our convolution activation functions we use ReLU, which is common and effective. The particular pooling layer we're using is a max pooling layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.layers import Activation\n",
    "from keras.layers.convolutional import Convolution2D, MaxPooling2D\n",
    "\n",
    "model.add(Convolution2D(\n",
    "        n_filters, \n",
    "        kernel_size=(n_conv, n_conv),\n",
    "        # we have a 28x28 single channel (grayscale) image\n",
    "        # so the input shape should be (28, 28, 1)\n",
    "        input_shape=(height, width, 1)\n",
    "))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.add(Convolution2D(n_filters, kernel_size=(n_conv, n_conv)))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "# then we apply pooling to summarize the features\n",
    "# extracted thus far\n",
    "model.add(MaxPooling2D(pool_size=(n_pool, n_pool)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can add dropout and our fully-connected (\"Dense\") and output (softmax) layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.layers import Dropout, Flatten, Dense\n",
    "\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "# flatten the data for the 1D layers\n",
    "model.add(Flatten())\n",
    "\n",
    "# Dense(n_outputs)\n",
    "model.add(Dense(128))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "# the softmax output layer gives us a probablity for each class\n",
    "model.add(Dense(n_classes))\n",
    "model.add(Activation('softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We tell Keras to compile the model using whatever backend we have configured (Theano or TensorFlow). At this stage we specify the loss function we want to optimize. Here we're using categorical cross-entropy, which is the standard loss function for multiclass classification.\n",
    "\n",
    "We also specify the particular optimization method we want to use. Here we're using Adam, which adapts the learning rate based on how training is going and improves the training process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss='categorical_crossentropy',\n",
    "    optimizer='adam',\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the model is defined and compiled, we can begin training and fit the model to our training data.\n",
    "\n",
    "Here we're training for only 10 epochs. This is plenty for this task, but for more difficult tasks, more epochs may be necessary.\n",
    "\n",
    "Training will take quite a while if you are running this on a CPU. Generally with neural networks, and especially with convolutional neural networks, you want to train on a GPU for much, much faster training times. Again, this dataset is relatively small so it won't take a terrible amount of time, but more than you might want to sit around and wait for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 79s - loss: 0.2837 - acc: 0.9136 - val_loss: 0.0586 - val_acc: 0.9803\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 79s - loss: 0.0970 - acc: 0.9706 - val_loss: 0.0396 - val_acc: 0.9869\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 80s - loss: 0.0758 - acc: 0.9778 - val_loss: 0.0391 - val_acc: 0.9870\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 79s - loss: 0.0619 - acc: 0.9814 - val_loss: 0.0343 - val_acc: 0.9892\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 79s - loss: 0.0530 - acc: 0.9837 - val_loss: 0.0353 - val_acc: 0.9878\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 80s - loss: 0.0487 - acc: 0.9848 - val_loss: 0.0303 - val_acc: 0.9898\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 80s - loss: 0.0425 - acc: 0.9868 - val_loss: 0.0310 - val_acc: 0.9897\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 80s - loss: 0.0385 - acc: 0.9881 - val_loss: 0.0288 - val_acc: 0.9909\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 82s - loss: 0.0340 - acc: 0.9896 - val_loss: 0.0330 - val_acc: 0.9901\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 79s - loss: 0.0341 - acc: 0.9887 - val_loss: 0.0295 - val_acc: 0.9915\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fa2921aa890>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many examples to look at during each update step\n",
    "batch_size = 128\n",
    "\n",
    "# how many times to run through the full set of examples\n",
    "n_epochs = 10\n",
    "\n",
    "# the training may be slow depending on your computer\n",
    "model.fit(X_train,\n",
    "          y_train,\n",
    "          batch_size=batch_size,\n",
    "          epochs=n_epochs,\n",
    "          validation_data=(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can evaluate the model on the test data to get a sense of how we did."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('loss:', 0.029475225206009783)\n",
      "('accuracy:', 0.99150000000000005)\n"
     ]
    }
   ],
   "source": [
    "# how'd we do?\n",
    "loss, accuracy = model.evaluate(X_test, y_test, verbose=0)\n",
    "\n",
    "print('loss:', loss)\n",
    "print('accuracy:', accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "99.06% accuracy on our test set. Not too bad for 10 minutes of training. Believe it or not, this would have been the best performing network for MNIST until around 2003. [As of today](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#4d4e495354) the best performing MNIST classifier achieves a 99.79% accuracy. We'll eventually need more innovations to get up to that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to make a prediction on a single sample, the following code will reshape it into a batch of size 1, output the probability of each classes, and find the index of the maximum probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('probabilities: ', array([  2.54888879e-15,   6.91964472e-11,   2.06181427e-09,\n",
      "         2.51610981e-08,   2.25821830e-13,   5.61656299e-14,\n",
      "         4.43780486e-19,   9.99999940e-01,   1.14250384e-12,\n",
      "         3.20635762e-09], dtype=float32))\n",
      "predicted = 7, actual = 7\n"
     ]
    }
   ],
   "source": [
    "x_sample = X_test[0].reshape(1,28,28,1)\n",
    "y_prob = model.predict(x_sample)[0]\n",
    "y_pred = y_prob.argmax()\n",
    "y_actual = y_test[0].argmax()\n",
    "\n",
    "print(\"probabilities: \", y_prob)\n",
    "print(\"predicted = %d, actual = %d\" % (y_pred, y_actual))"
   ]
  }
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